A long-standing need exists for biologists, forest managers, and others to have information that characterizes a set of vegetation, such as a stand of trees. Traditionally, attributes of a sample of the vegetation are manually obtained and extrapolated to a larger set of vegetation. For example, sampling may be performed to assess the vegetation's height, volume, age, biomass, and species, among other attributes. This information that characterizes the attributes of the vegetation may be used in a number of different ways. For example, the sample data may be used to quantify the inventory of raw materials that are available for harvest. By way of another example, by comparing attributes of a sample set of vegetation over time, one may determine whether a disease is compromising the health of the vegetation.
Unfortunately, extrapolating sample data to a larger set may not accurately reflect the actual attributes of the vegetation. In this regard, the species and other vegetation attributes may depend on a number of different factors that are highly variable even in nearby geographic locations. As a result, biologists, forest managers, and others may not have information that accurately characterizes the attributes of vegetation.
Advancements in airborne and satellite laser scanning technology provide an opportunity to obtain more accurate information about the attributes of vegetation. In this regard, Light Detection and Ranging (“LiDAR”) is an optical remote scanning technology used to identify distances to remote targets. For example, a laser pulse may be transmitted from a source location, such as an aircraft or satellite, to a target location on the ground. The distance to the target location may be quantified by measuring the time delay between transmission of the pulse and receipt of one or more reflected return signals. Moreover, the intensity of a reflected return signal may provide information about the attributes of the target. In this regard, a target on the ground will reflect return signals in response to a laser pulse with varying amounts of intensity. For example, a species of vegetation with a high number of leaves will, on average, reflect return signals with higher intensities than vegetation with a smaller number of leaves.
LiDAR optical remote scanning technology has attributes that make it well-suited for identifying the attributes of vegetation. For example, the wavelengths of a LiDAR laser pulse are typically produced in the ultraviolet, visible, or near infrared areas of the electromagnetic spectrum. These short wavelengths are very accurate in identifying the horizontal and vertical location of leaves, branches, etc. Also, LiDAR offers the ability to perform high sampling intensity, extensive aerial coverage, as well as the ability to penetrate the top layer of a vegetation canopy. In this regard, a single LiDAR pulse transmitted to target vegetation will typically produce a plurality of return signals that each provide information about attributes of the vegetation.
A drawback of existing systems is an inability to differentiate between individual trees, bushes, and other vegetation that are represented in a set of LiDAR data. For example, raw LiDAR data may be collected in which a forest is scanned at a high sampling intensity sufficient to produce data that describes the position and reflective attributes of different points in the forest. It would be beneficial to have a system in which the raw LiDAR data is processed to differentiate the points represented in the LiDAR data and allocate those points to individual items of vegetation.
It would also be beneficial to have a system capable of identifying various attributes of vegetation from raw LiDAR data. For example, with a high enough sampling rate, the shape and other properties of a tree's crown, branches, and leaves may be discernible. If this type of information was discernable, computer systems may be able to identify the species of individual items of vegetation.